GPT-5.2-Codex vs Kimi K3.1 Pro

Pick up to 3 models · pricing, context and capabilities · simulate your product cost

Data updated on November 14, 2026· 230 models

OpenAIGPT-5.2-Codex

GPT-5.2-Codex is an upgraded version of GPT-5.1-Codex optimized for software engineering and coding workflows.

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Reasoning estimated
Speed estimated
▲
Input
Output
Context
400 K tokens
Max output
128 K
Input, $ per 1M tokens
▲$1.75
Output, $ per 1M tokens
$14.00
Cache read, $ per 1M
$0.175
Batch (input / output)
—
Tool calling
Yes
Released
12/11/2025
Knowledge cutoff
2025-08-31
API id
openai/gpt-5.2-codex
MoonshotK3.1 Pro

Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model and a post-trained successor to Kimi K3, with a 1M-token context window.

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Reasoning estimated
▲
Speed estimated
Input
Output
Context
▲1 M tokens
Max output
▲944 K
Input, $ per 1M tokens
$2.00
Output, $ per 1M tokens
▲$10.00
Cache read, $ per 1M
$0.200
Batch (input / output)
$0.700 / $6.00
Tool calling
Yes
Released
▲10/23/2026
Knowledge cutoff
—
API id
moonshotai/kimi-k3.1-pro
Third model (optional)
text image audio video PDF / file reasoning (1 to 5, estimated by family) speed (1 to 4, estimated by family)▲ best value among the selected

Which is the better API choice: GPT-5.2-Codex vs Kimi K3.1 Pro?

  • GPT-5.2-Codex: cheapest input (13% less than Kimi K3.1 Pro)
  • Kimi K3.1 Pro: cheapest output
  • Kimi K3.1 Pro: largest context (1 M)
  • Kimi K3.1 Pro: the newest (10/23/2026)
  • Kimi K3.1 Pro: most reasoning (estimated)
  • GPT-5.2-Codex: the fastest (estimated)

Pricing is split: GPT-5.2-Codex wins on input and Kimi K3.1 Pro wins on output. Kimi K3.1 Pro accepts more context (1 M). Kimi K3.1 Pro offers a batch tier at half price for jobs that do not need an immediate answer.

What would your product cost?

Estimate the monthly cost with your app's real volume. List prices, no cache or batch discounts.

GPT-5.2-Codex
$82.25/mo
Kimi K3.1 Pro
$70.00/mo

With Kimi K3.1 Pro you save $12.25 a month versus GPT-5.2-Codex (15% less).

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Paste your prompt: tokens and cost per model

Rough estimate (about 4 characters per token). Nothing is sent to any server.

76tokens approx.287 characters

Cost of sending it 1,000 times (input only)

  1. 1Claude Haiku 5.5 Anthropic$0.008
  2. 2GPT-5.2-Codex OpenAI$0.133
  3. 3Kimi K3.1 Pro Moonshot$0.152
  4. 4Claude Sonnet 5.5 Anthropic$0.152
  5. 5Claude Opus 5.5 Anthropic$0.304
  6. 6Claude Opus 5 Anthropic$0.380
  7. 7Claude Fable 5.2 Anthropic$0.608
  8. 8Claude Fable 5.1 Anthropic$0.760

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Frequently asked questions

Which is the better API choice: GPT-5.2-Codex vs Kimi K3.1 Pro?

Pricing is split: GPT-5.2-Codex wins on input and Kimi K3.1 Pro wins on output. Kimi K3.1 Pro accepts more context (1 M). Kimi K3.1 Pro offers a batch tier at half price for jobs that do not need an immediate answer.

Which is cheaper: GPT-5.2-Codex and Kimi K3.1 Pro?

GPT-5.2-Codex is cheaper: $1.75 per million input tokens and $14.00 per million output tokens, versus $2.00 / $10.00 for Kimi K3.1 Pro.

Which has more context: GPT-5.2-Codex and Kimi K3.1 Pro?

Kimi K3.1 Pro accepts 1 M tokens of context; GPT-5.2-Codex accepts 400 K.

Which is newer: GPT-5.2-Codex and Kimi K3.1 Pro?

Kimi K3.1 Pro was released on 10/23/2026; GPT-5.2-Codex on 12/11/2025.

What would 10,000 requests a month cost with GPT-5.2-Codex and Kimi K3.1 Pro?

With 1,500 input and 400 output tokens per request: GPT-5.2-Codex: $82.25; Kimi K3.1 Pro: $70.00 a month, at list price.

Where do the prices come from and how often are they updated?

From the public catalogs of OpenRouter, models.dev and LiteLLM, which publish each provider's list prices. They sync once a day and the last update date is shown above the comparison.

What does price per million tokens mean?

It is what the provider charges per million tokens you send (input) or the model generates (output). A token is roughly 4 characters. Output almost always costs more than input.

Sources: OpenRouter, models.dev and LiteLLM. Prices in USD per million tokens, list price, no volume discounts. Prompt tokens are an estimate; each provider's tokenizer may differ.